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arXiv 2024年

作者： Sri Darshan, M. Nithinraj, N. Jaisachin, B. Department of Artificial Intelligence and Machine Learning SRM University Chennai India Department of Computer Science SRM University Chennai India

Predictive modeling and time-pattern analysis are increasingly critical in this swiftly shifting retail environment to improve operational efficiency and informed decision-making. This paper reports a comprehensive application of state-of-the-art machine learning to the retailing domain with a specific focus on association rule mining, sequential pattern mining, and time-series forecasting. Association rules: Relationship Mining This provides the key product relationships and customer buying patterns that form the basis of individually tailored marketing campaigns. Sequential pattern mining: Using the PrefixSpan algorithm, it identifies frequent sequences of purchasing products-extremely powerful insights into consumer behavior and also better management of the inventories. What is applied for sales trend forecasting models Prophet applies on historical transaction data over seasonality, holidays, and long-term growth. The forecast results allow predicting demand variations, thus helping in proper inventory alignment and avoiding overstocking or understocking of inventory. Our results are checked through the help of metrics like MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) to ensure our predictions are strong and accurate. We will combine the aspects of all of these techniques to prove how predictive modeling and temporal pattern analysis can help optimize control over inventory, enhance marketing effectiveness, and position retail businesses as they rise to ever greater heights. This entire methodology demonstrates the flexibility with which data-driven strategies can be leveraged to revitalize traditional retailing practices. Copyright © 2024, The Authors. All rights reserved.

关键词： Sales

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Early Detection of Black Fungus Using Deep learning Models for Efficient Medical Diagnosis 4

Early Detection of Black Fungus Using Deep Learning Models f...

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4th IEEE International Conference on Emerging Techniques in Computational intelligence, ICETCI 2024

作者： Hassan, Esraa Saber, Abeer El-Kenawy, El-Sayed M. Bhatnagar, Roheet Shams, Mahmoud Y. Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Machine Learning Kafrelsheikh33516 Egypt Damietta University Faculty of Computers and Artificial intelligence Information technology Dept. Damietta34517 Egypt Delta Higher Institute of Engineering and Technology Dept. of Communications and Electronics Mansoura35111 Egypt Manipal University Department of Computer Science and Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350387063

Mucormycosis, also known as black fungus, is a rare infection caused by mould that can affect the lungs, brain, skin, and sinuses. People with weakened immune systems due to underlying health conditions (e.g., organ transplant, uncontrolled diabetes, cancer, neutropenia, AIDS, and COVID-19) or drug use are more likely to contract the infection. Recent reports have indicated that a small number of COVID-19 survivors with co-morbidities are particularly vulnerable to black fungus. Therefore, recovered COVID-19 patients should seek medical attention immediately if they experience any mucormycosis symptoms. Patients with COVID-19 have numerous concerns regarding this life-threatening infection, particularly those with immunocompromised states such as solid organ transplant (SOT) and hematopoietic stem cell transplant (HSCT) or tumors, who are more likely to be infected. Early diagnosis and treatment are essential to prevent patient mortality. In this paper, we present the Multi-Class Black Fungus (MCBF) dataset, which consists of three compressed folders containing images. The first folder includes 66 images of eye black fungus after data preprocessing, while the second folder contains 63 images of mouth black fungus after data preprocessing. The third contains 62 images of the mouth after applying data preprocessing, and the last one contains 79 images of black skin fungus after applying data preprocessing. The MCBF dataset contains images that aid in training and validation when using deep learning algorithms to recognise and classify black fungus diseases, then we apply a resnet(50) pretrained model by using MCBF accuracy reaches 96.12%. © 2024 IEEE.

关键词： COVID-19

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Optimal parameter estimation of three solar cell models using modified spotted hyena optimization

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Journal of Ambient intelligence and Humanized Computing 2024年第1期15卷 361-372页

作者： Gafar, Mona El-Sehiemy, Ragab A. Hasanien, Hany M. Abaza, Amlak Department of Computer Science College of Science and Humanities in Al-Sulail Prince Sattam Bin Abdulaziz University Kharj Saudi Arabia Machine Learning and Information Retrieval Department Artificial Intelligence Kafrelsheikh University Kafr El Sheikh Egypt Electrical Engineering Department Faculty of Engineering Kafrelsheikh University Kafr El Sheikh Egypt Electrical Power and Machines Department Faculty of Engineering Ain Shams University Cairo11517 Egypt

This paper is concerned with identifying the optimal parameters of solar cell by using a modified spotted hyena optimization algorithm (MSHOA). In the MSHOA, the optimization process initializes random search agents then selects the best agents. The MSHOA modifies the original spotted hyena optimization algorithm by using an accelerating function, which improves the performance of reaching the optimal solution. The convergence occurs rapidly and realizes the global optimization with small iterations number. The numerical results obtained with different models emphasize that the MSHOA has the ability and stability to estimate the global optimal decision variables of solar cell module with a minimal root mean square error compared with other algorithms existed in literature. Moreover, three electrical models called single diode model, double diode model and triple diode model TDM are accurate for depicting electrical behavior of PV modules. It is truth to say that TDM is the most accurate model among the three models. The significant agreements between the estimated model using MSHOA, at different operating temperatures and irradiances, and the measured data assert on the effectiveness of the proposed algorithm and the accuracy of PV model. All results emphasize that the MSHOA is an effective and reliable optimization algorithm for finding the optimal parameters of solar cell modules. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Solar panels

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Escaping Strategy with Golden Jackal Optimization Based Clustering and Routing in Mobile Ad Hoc Network 2

Escaping Strategy with Golden Jackal Optimization Based Clus...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Alkhayyat, Ahmad Kumar, Komuravelly Sudheer Hiremath, Murigendrayya M. Das, Abhijeet Madhura, G.K. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq School of Computer Science & Artificial Intelligence Sr University Warangal India Dayananda Sagar College of Engineering Department of Medical Electronics Engineering Bengaluru India Department of Civil Engineering Odisha Bhubaneswar India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350373110

The Mobile Ad Hoc Network (MANET) is organized with various nodes which communicates wirelessly. Therefore, the communication in MANET suffered from different problems like low stability, high energy consumption and link failure. Additionally, the path failure happens in network because of unrestricted movement of node, that maximized energy usage, delay and minimized nodes lifetime. In this research, the Escaping Strategy with Golden Jackal Optimization (ES-GJO) algorithm is developed for cluster head selection and routing in the MANET network. The appropriate Cluster Head (CH) and routes are chosen using ES-GJO algorithm that boosts network lifetime and predicts link failure with less energy consumption. The ES-GJO algorithm is estimated with metrics of Packet Delivery Ratio (PDR), Delay and Energy consumption for 100 to 500 nodes. The proposed ES-GJO algorithm attained highest PDR of 99.87%, 99.83%, 99.65%, 98.76% and 97.38% for nodes 100, 200, 300, 400 and 500 which is efficient than Eagle Based Density Clustering (EBDC) and Optimal Route Selection Mobile Ad hoc Networks (ORSMAN) algorithms. © 2024 IEEE.

关键词： Mobile ad hoc networks

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DTN Routing Algorithm Based on Social Center and Classifier 20th

DTN Routing Algorithm Based on Social Center and Classifie...

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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Cui, Jianqun Gao, Mengnan Chang, Yanan Yan, Huiran Ma, Zhiyuan Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Hubei Wuhan430079 China Department of Computer Science Central China Normal University Wuhan China

ISBN: (纸本)9789819628636

Opportunistic Mobile Social Networks (OMSNs) are intermittent complex networks consisting of mobile nodes equipped with communication devices, characterized by unstable connections, high node mobility, and limited resources. Existing OSMN routing algorithms, when selecting the next hop node, either focus on limited information within the network and employ simple fusion techniques or consider excessive redundant information in complex designs. To address these issues, this paper proposes an OSMN routing algorithm RFSC based on social centrality and classifier. The algorithm introduces a social circle partitioning algorithm and designs the routing algorithm SCRouter by considering the social attributes of nodes, node-specific situations, and message properties. Subsequently, the SCRouter algorithm is integrated with classifier to enhance the algorithm’s anti-interference capabilities and reduce the consideration of redundant metrics. Furthermore, the RFSC algorithm introduces an adaptive cache replica allocation strategy to improve data transmission efficiency. Experimental results demonstrate that compared to CARA, Prophet, SprayAndWait, CPN_ASW and INR_PRA algorithms, the RFSC enhances delivery rates by 139.6%, 16.6%, 39.5%, 12.0% and 12.7% respectively. It effectively reduces transmission delays and alleviates network congestion. © IFIP International Federation for Information Processing 2025.

关键词： Routing algorithms

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Computationally Efficient machine learning Methodology for Indian Nobel Laureate Classification

Computationally Efficient Machine Learning Methodology for I...

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IEEE Recent Advances in Intelligent Computational Systems (RAICS)

作者： Swastik Banerjee Helen. K. Joy Department of Computer Science Artificial Intelligence & Machine Learning CHRIST Deemed to be University Bengaluru INDIA Department of Computer Science CHRIST Deemed to be University Bengaluru INDIA

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381696

A computationally efficient methodology for Indian Nobel Laureate classification is proposed in this study, emphasizing the optimization of image categorization through supervised learning techniques. Leveraging advancements in Convolutional Neural Networks (CNNs), the research aims to enhance the efficiency and precision of image classification tasks. The study utilizes Logistic Regression for dataset analysis, initially employing browser extensions for mass downloading categorized image data. Haar cascade classifiers are then used for data wrangling, focusing on facial, nose, and mouth recognition. Following this, feature engineering through wavelet transformation reduces image dimensionality, preparing the dataset for the chosen ML model, Logistic Regression. The primary focus is to simplify technology for improved image categorization. Support Vector machines (SVM), Random Forest, and Logistic Regression are examined, with Logistic Regression emerging as the most effective model, achieving an accuracy rate of 87.5%. A thorough evaluation using Confusion Matrices reveals Logistic Regression's superior performance in classifying images of Indian Nobel laureates. A strategic up-sampling approach is implemented to address dataset inconsistencies, ensuring balanced representation across classes. The Haar wavelet transform is then applied for feature extraction, optimizing the dataset for ML models. The dataset is split into training and testing sets (80–20), and the three models are trained and evaluated for accuracy. Logistic Regression proves to be the best performer, offering insights into prominent leaders' identification. The research offers a detailed pipeline for data preprocessing, feature engineering, and model assessment, culminating in a robust image categorization system. Logistic Regression emerges as a reliable method for biographical picture identification, demonstrating superior accuracy over SVM and Random Forest. This research underscores the import

关键词： Support vector machines Wavelet transforms Training Logistic regression Accuracy Biological system modeling Pipelines Image categorization Random forests Testing

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A Comprehensive Study on Parkinson's Disease Detection Through machine learning Techniques

A Comprehensive Study on Parkinson's Disease Detection Throu...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Burle, Rushikesh Gaurkhede, Sanskruti Fulkar, Bhushan Gundewar, Swapnil Pacharaney, Utkarsha Verma, Prateek Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Computer Science and Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Data Science Faculty of Engineering and Technology Mahartashtra Wardha442001 India

ISBN: (纸本)9798350375237

One of the biggest issues with worldwide public health is Parkinson's disease (PD). It is usually known that about are around one million Americans who have Parkinson's disease, whereas there are approximately five million cases of the condition globally. As a result, it's critical to identify Parkinson's disease early on and make the appropriate treatment plans. These days, machine learning is essential for analysing problems in real time and offering answers thanks to its widely used algorithms. These days, machine learning algorithms are used in the healthcare industry to identify patients' health problems. This research went into great detail about the methods used using algorithms that use machine learning to classify Parkinson's disease. A fault in the brain cells that generate dopamine, a necessary chemical for synapses to connect with one another, is the cause of Parkinson's disease. The brain's dopamine-producing neurons are in charge of control, flexibility, and event familiarity. When 60-80% of these cells disappear, inadequate dopamine is produced, which causes Parkinson's disease symptoms to manifest. In this case, the Random Forest, Decision Tree, K-Nearest Neighbours, and Support Vector machine techniques were used to identify the illness. Compared to Decision Tree and Support Vector machine, Random Forest and K-Nearest Neighbours offer the best performance. © 2024 IEEE.

关键词： Support vector machines

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Prediction of Heart Disease Using machine learning Algorithms 2

Prediction of Heart Disease Using Machine Learning Algorithm...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Burle, Rushikesh Gaurkhede, Sanskruti Dewangan, Leelkanth Pacharaney, Utkarsha Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Computer Science and Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798350360523

In India, heart disease claims the lives of about one person every day. The human body's most vital organ is the heart. The heart has two primary purposes: first, it gathers blood from body tissues and pumps it to the lungs;second, it gathers blood from the lungs and pumps it to all body *** disease has claimed the lives of many people. Predicting that disease at the appropriate moment is crucial, for this reason. Making use of machine learning Additionally, illnesses are easily identified and anticipated. It also covers the use of many algorithms on the Heart Disease Dataset. The dataset used in this research was obtained from Kaggle. Analyzing fundamental machine learning algorithms for the prediction of heart disease, such as Random Forest, K-Nearest Neighbours, Decision Tree Classifier, and Support Vector machine, is the primary goal of this study. Our Acquired Model for The Decision Tree Classifier has a score of 79.0%. And the Random Forest Classifier has an 84.0% score. With a linear kernel, the Support Vector Classifier scores 83.0%. Next, K Neighbours Classifier~ outperformed all previously employed methods, achieving the maximum score of {87%} with ~8 nearest result. RF (Random Forest), K-Nearest Neighbours Algorithm, and Decision Tree, together with Python programming and Visual Studio, which excels at both data and code. © 2024 IEEE.

关键词： Heart

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Smart Healthcare: Harnessing the Power of machine learning for Predictive Analysis 6

Smart Healthcare: Harnessing the Power of Machine Learning f...

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6th Parul International Conference on Engineering and Technology, PICET 2024

作者： Barhate, Aditya Kumar, Praveen Verma, Prateek Jikar, Nayan Tale, Abhay Hikre, Vaibhav Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India

ISBN: (纸本)9798350369748

Smart healthcare employs cloud computing, the Internet of Things (IoT), and supercomputers to manage the healthcare system. Medical data storage volume is increasing, necessitating the adoption of machine learning (ML) models for predicting outcomes, particularly in the treatment of chronic illnesses. The trend of care personalization and technological integration is highlighted by the development of ML applications for clinical decision support systems, including real-time predictive analytics. Predictive analytics applications employ ML and artificial intelligence (AI) to forecast patient risks, outcomes, and treatment responses. Predictive analytics is more important in healthcare when it comes to detecting Alzheimer's disease, cardiovascular events, radiology, and other conditions. Therefore, the current study highlights the change in illness identification, medical imaging, and personalized medication brought about by ML and Predictive Analytics in Precision Medicine (PM). However, ethical concerns and the deployment of transparent AI models still need more research. This study anticipates a time when healthcare will be quicker, better informed, and more patient-centered by using the insights provided by intelligent healthcare data. By effectively saving patient lives, the use of ML in healthcare ushers in a new era for the industry. © 2024 IEEE.

关键词： Disease Detection machine learning Medical Imaging Predictive Analytics Smart Healthcare

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Deep Reinforcement learning In Autonomous Cars To Preventing Road Accident 2

Deep Reinforcement Learning In Autonomous Cars To Preventing...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Thote, Rushikesh Barhate, Aditya Tale, Abhay Verma, Prateek Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India

ISBN: (纸本)9798331528713

The integration of autonomous vehicles (AVs) into modern transportation systems presents both unprecedented opportunities and challenges, particularly in ensuring road safety and reducing accidents. This paper explores the application of Deep Reinforcement learning (DRL) techniques to enhance decision-making processes in AVs, aiming to proactively prevent road accidents. By simulating complex driving environments, DRL algorithms enable AVs to learn optimal driving strategies through continuous interaction and feedback. We introduce a DRL-based framework that incorporates sensor data fusion, real-time environment analysis, and adaptive control mechanisms. Experimental results from simulated urban and highway scenarios demonstrate a significant reduction in collision rates and improved response to unforeseen obstacles compared to traditional rule-based systems. The findings underscore the potential of DRL in fortifying the reliability and safety of autonomous driving technologies. © 2024 IEEE.

关键词： Deep reinforcement learning

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